The term “additive manufacturing” refers to processes used to synthesize three-dimensional objects in which successive layers of material are formed by a manufacturing machine under computer control to create an object using digital model data from a 3D model. One example of additive manufacturing is direct metal laser sintering (DMLS), which uses a laser fired into a bed of powdered metal, with the laser being aimed automatically at points in space defined by a 3D model, thereby melting the material together to create a solid structure. The term “direct metal laser melting” (DMLM) may more accurately reflect the nature of this process since it typically achieves a fully developed, homogenous melt pool and fully dense bulk upon solidification. The nature of the rapid, localized heating and cooling of the melted material enables near-forged material properties, after any necessary heat treatment is applied.
The DMLS process uses a 3D computer-aided design (CAD) model of the object to be manufactured, whereby a CAD model data file is created and sent to the fabrication facility. A technician may work with the 3D model to properly orient the geometry for part building and may add supporting structures to the design, as necessary. Once this “build file” has been completed, it is “sliced” into layers of the proper thickness for the particular DMLS fabrication machine and downloaded to the machine to allow the build to begin. The DMLS machine uses, e.g., a 200 W Yb-fiber optic laser. Inside the build chamber area, there is a material dispensing platform and a build platform along with a recoater blade used to move new powder over the build platform. The metal powder is fused into a solid part by melting it locally using the focused laser beam. In this manner, parts are built up additively layer by layer—typically using layers 20 micrometers thick. This process allows for highly complex geometries to be created directly from the 3D CAD data, automatically and without any tooling. DMLS produces parts with high accuracy and detail resolution, good surface quality, and excellent mechanical properties.
Defects, such as subsurface porosity, can occur in DMLM processes due to various machine, programming, environment, and process parameters. For example, deficiencies in machine calibration of mirror positions and laser focus can result in bulk-fill laser passes not intersecting edge-outline passes. Such deficiencies can result in unfused powder near the surface of the component, which may break through the surface to cause defects which cannot be healed by heat treatment finishing processes. Another subsurface effect of deficiencies in calibration and machine programming is excessive dwelling at the turnaround points in raster scanning. Such dwell can result in excessive laser time (i.e., laser focus time) on a given volume, which may result in “key-holing” and subsurface porosity. Laser and optics degradation, filtration, and other typical laser welding effects can also significantly impact process quality, particularly when operating for dozens or hundreds of hours per build. Furthermore, the DMLM process has thermal shrink and distortion effects which may require CAD model corrections to bring part dimensions within tolerance.
To reduce such defects, process models could be used to predict local geometric thermal cycles during the DMLM process, thereby predicting material structure (e.g., grain) and material properties. Models could also be used to predict shrink and distortion, which would enable virtual iteration prior to the actual DMLM process. Furthermore, predictive models could be used to generate a compensated DMLM model to drive the manufacturing machine while accounting for such shrinkage and distortion.
Conventional models relating build parameters to expected melt pool characteristics can be incomplete and/or inaccurate because of shortcomings in the collection and use of data from sensors monitoring the manufacturing process. Many of these shortcomings arise because conventional manufacturing sensor configurations produce “context-less” data, i.e., data which does not have a frame of reference other than time of measurement.
Consequently, captured manufacturing sensor data is not in a useful context for design teams to analyze. Another shortcoming of conventional manufacturing sensor configurations is that the sensors monitoring a DMLM process may produce unmanageable quantities of data. For example, a pyrometer monitoring a build may have a data acquisition rate of 50 kHz, which means that a tremendous quantity of data is produced over a long build, e.g., 30 hours.